{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fossil-paper",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "affecting-passion",
   "metadata": {},
   "outputs": [],
   "source": [
    "#加载数据表体测分数_女生.xls\n",
    "df_female = pd.read_excel('./体测分数_女生.xls')\n",
    "df_female_800m = df_female['女800米跑']\n",
    "df_female_ty = df_female['女跳远']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "peaceful-tours",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([588.,   0.,   0.,   5.]), array([   0., 1003., 2006., 3009., 4012.]))"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对女800米跑进行直方图绘制统计各分数段人数，分成4份\n",
    "count,bins,fig= plt.hist(df_female_800m,bins = 4,color='blue',density=False)\n",
    "count,bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "included-letters",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([588,   0,   0,   5], dtype=int64),\n",
       " array([   0., 1003., 2006., 3009., 4012.]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#单独提取统计频次和各分数区间\n",
    "np.histogram(df_female_800m,bins = 4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "preliminary-danger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 15.,   1., 317., 260.]), array([  0.,  55., 110., 165., 220.]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对女跳远进行直方图绘制统计各分数段人数，分成4份\n",
    "count,bins,fig= plt.hist(df_female_ty,bins = 4,color='green',density=False)\n",
    "count,bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aquatic-melissa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 15,   1, 317, 260], dtype=int64),\n",
       " array([  0.,  55., 110., 165., 220.]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#单独提取统计频次和各分数区间\n",
    "np.histogram(df_female_ty,bins = 4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "elder-politics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     男1000米跑\n",
       " 0       4.13\n",
       " 1       4.16\n",
       " 2       4.09\n",
       " 3       4.21\n",
       " 4       3.44\n",
       " 5       3.49\n",
       " 6       3.54\n",
       " 7       4.03\n",
       " 8       4.01\n",
       " 9       4.12\n",
       " 10      4.00\n",
       " 11      4.13\n",
       " 12      3.45\n",
       " 13      3.46\n",
       " 15      3.57\n",
       " 16      4.18\n",
       " 17      3.32\n",
       " 18      3.56\n",
       " 19      3.47\n",
       " 20      3.53\n",
       " 21      3.57\n",
       " 22      3.42\n",
       " 23      4.03\n",
       " 24      4.14\n",
       " 25      4.04\n",
       " 26      4.04\n",
       " 27      4.02\n",
       " 28      3.57\n",
       " 29      4.16\n",
       " 30      4.01\n",
       " ..       ...\n",
       " 444     4.07\n",
       " 445     3.56\n",
       " 446     4.07\n",
       " 447     4.15\n",
       " 448     4.36\n",
       " 449     3.48\n",
       " 450     3.58\n",
       " 451     4.02\n",
       " 452     4.02\n",
       " 453     4.38\n",
       " 455     4.02\n",
       " 456     4.26\n",
       " 457     4.09\n",
       " 458     3.49\n",
       " 459     4.36\n",
       " 460     4.37\n",
       " 461     3.44\n",
       " 462     3.55\n",
       " 463     3.41\n",
       " 464     5.29\n",
       " 465     4.11\n",
       " 466     4.56\n",
       " 467     3.54\n",
       " 469     4.04\n",
       " 470     3.54\n",
       " 471     4.58\n",
       " 472     4.23\n",
       " 473     5.19\n",
       " 474     3.25\n",
       " 475     4.39\n",
       " \n",
       " [456 rows x 1 columns],\n",
       "      男引体\n",
       " 0      1\n",
       " 1      7\n",
       " 2      1\n",
       " 3      1\n",
       " 4      9\n",
       " 5      7\n",
       " 6      7\n",
       " 7      1\n",
       " 8     10\n",
       " 9     11\n",
       " 10    11\n",
       " 11    15\n",
       " 12     3\n",
       " 13     7\n",
       " 15     5\n",
       " 16     4\n",
       " 17    12\n",
       " 18    12\n",
       " 19    16\n",
       " 20     7\n",
       " 21     2\n",
       " 22    15\n",
       " 23     3\n",
       " 24     1\n",
       " 25     5\n",
       " 26     5\n",
       " 27    12\n",
       " 28    11\n",
       " 29     7\n",
       " 30    11\n",
       " ..   ...\n",
       " 442    4\n",
       " 443    7\n",
       " 444    7\n",
       " 445   13\n",
       " 446   10\n",
       " 447    2\n",
       " 448   11\n",
       " 449   12\n",
       " 450   11\n",
       " 451    7\n",
       " 452    4\n",
       " 453    8\n",
       " 455    8\n",
       " 456    8\n",
       " 457    7\n",
       " 458   10\n",
       " 459   20\n",
       " 460    1\n",
       " 461    7\n",
       " 462   10\n",
       " 463    5\n",
       " 465   14\n",
       " 466   13\n",
       " 467    9\n",
       " 469    1\n",
       " 470   11\n",
       " 471    9\n",
       " 473    6\n",
       " 474   13\n",
       " 475   11\n",
       " \n",
       " [442 rows x 1 columns])"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#加载数据表体测分数_男生.xls，并提取1000米跑和引体数据，过滤掉成绩为0 的无效数据\n",
    "df_male = pd.read_excel('./体测分数_男生.xls')\n",
    "df_male_1000m = df_male[df_male['男1000米跑']>0][['男1000米跑']]\n",
    "\n",
    "df_male_yt = df_male[df_male['男引体']>0][['男引体']]\n",
    "df_male_1000m,df_male_yt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "duplicate-fence",
   "metadata": {},
   "outputs": [],
   "source": [
    "#男1000米跑等宽分箱\n",
    "df_male_1000m['男1000米跑等宽分箱'] = pd.cut(df_male_1000m['男1000米跑'],3,labels = list('优中差'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "legislative-concrete",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "中    245\n",
       "优    195\n",
       "差     16\n",
       "Name: 男1000米跑等宽分箱, dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#频次计算\n",
    "df_male_1000m_cutcount=df_male_1000m['男1000米跑等宽分箱'].value_counts()\n",
    "df_male_1000m_cutcount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "selective-zealand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#男1000米跑饼图绘制\n",
    "labels = list(df_male_1000m_cutcount.index)\n",
    "plt.figure(figsize = (5,5))\n",
    "_ = plt.pie(df_male_1000m_cutcount,\n",
    "            labels=labels,\n",
    "            textprops={'family':'KaiTi','fontsize':18},\n",
    "           autopct='%0.2f%%',explode=[0,0,0.15], shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "inside-matrix",
   "metadata": {},
   "outputs": [],
   "source": [
    "#男引体等宽分箱\n",
    "df_male_yt['男引体等宽分箱'] = pd.cut(df_male_yt['男引体'],3,labels = list('差中优'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "superb-trance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(差    223\n",
       " 中    199\n",
       " 优     20\n",
       " Name: 男引体等宽分箱, dtype: int64,\n",
       " ['差', '中', '优'])"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#男引体等宽分箱\n",
    "df_male_yt_cutcount=df_male_yt['男引体等宽分箱'].value_counts()\n",
    "df_male_yt_cutcount,list(df_male_yt_cutcount.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "editorial-feeling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#男引体跑饼图绘制\n",
    "labels = list(df_male_yt_cutcount.index)\n",
    "plt.figure(figsize = (5,5))\n",
    "_ = plt.pie(df_male_yt_cutcount,\n",
    "            labels=labels,\n",
    "            textprops={'family':'KaiTi','fontsize':18},\n",
    "           autopct='%0.2f%%',explode=[0,0,0.15], shadow=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "interracial-germany",
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = df_male['BMI']#外圈\n",
    "p2 = df_female['BMI']##内圈\n",
    "p1.round(1).sort_values().to_csv('./1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "annual-blend",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23.2"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(23.25,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "veterinary-roman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(466,\n",
       " array([357,  58,  38,  13], dtype=int64),\n",
       " 正常     357\n",
       " 超重      58\n",
       " 肥胖      38\n",
       " 低体重     13\n",
       " Name: 等级, dtype: int64,\n",
       " ['正常', '超重', '肥胖', '低体重'])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['低体重','正常','超重','肥胖']\n",
    "\n",
    "# (1).男生BMI指定宽度分箱\n",
    "df_male['等级'] = pd.cut(df_male['BMI'].round(1),#分箱数据\n",
    "                       #----此处有坑，round()四舍五入方式，日后根据需求而定\n",
    "    bins = [0,16.4,23.2,26.3,100],#分箱断点\n",
    "    right = True,\n",
    "    labels=labels)# 分箱后分类\n",
    "p_male = df_male['等级'].dropna() #过滤空值\n",
    "p1 = np.array(p_male.value_counts())#统计各类别数量\n",
    "p1.sum(),p1,p_male.value_counts(),list(p_male.value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "generous-cause",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(466, array([357,  58,  38,  13], dtype=int64))"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1 = np.array(p_male.value_counts())\n",
    "p1.sum(),p1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "portable-serve",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(586,\n",
       " array([445,  83,  48,  10], dtype=int64),\n",
       " 正常     445\n",
       " 超重      83\n",
       " 肥胖      48\n",
       " 低体重     10\n",
       " Name: 等级, dtype: int64,\n",
       " ['正常', '超重', '肥胖', '低体重'])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ['低体重','正常','超重','肥胖']\n",
    "## (2).女生BMI指定宽度分箱\n",
    "df_female['等级'] = pd.cut(df_female['BMI'].round(1),#分箱数据\n",
    " bins = [0,16.4,22.7,25.2,100],#分箱断点\n",
    " right = True,\n",
    " labels=labels)# 分箱后分类\n",
    "p_female = df_female['等级'].dropna()#过滤空值\n",
    "p2 = np.array(p_female.value_counts())#统计各类别数量\n",
    "p2.sum(),p2,p_female.value_counts(),list(p_female.value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "administrative-spencer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7,7))\n",
    "\n",
    "_p1,_1,_2 = plt.pie(p1,radius=1,\n",
    "            autopct='%0.2f%%',\n",
    "            pctdistance=0.85,#将百分比的数值挪到外圈宽里面\n",
    "            labels =list(p_male.value_counts().index),\n",
    "            wedgeprops={'linewidth':5,#外圈线宽\n",
    "                   'width':0.3, #整个饼图宽度\n",
    "                   'edgecolor':'white'#间隔线颜色\n",
    "                  },\n",
    "            textprops={'family':'KaiTi','fontsize':18},\n",
    "                    colors = ['orange','yellow','green','red']\n",
    "       )\n",
    "\n",
    "_p2,_1,_2 = plt.pie(p2,radius=0.7,\n",
    "            autopct='%0.2f%%',\n",
    "            pctdistance=0.55,#将百分比的数值挪到外圈宽里面\n",
    "#             labels =list(p_female.value_counts().index),\n",
    "            wedgeprops={'linewidth':5,#外圈线宽\n",
    "                   'width':0.7, #整个饼图宽度\n",
    "                   'edgecolor':'white'#间隔线颜色\n",
    "                  },\n",
    "            textprops={'family':'KaiTi','fontsize':18},\n",
    "                    colors = ['orange','yellow','green','red']\n",
    "       )\n",
    "plt.rcParams['font.family'] = 'KaiTi'  #全局设置字体\n",
    "# plt.reParams['font.size'] = 18#全局设置字体大小\n",
    "_00 = plt.legend(_p1,\n",
    "                 labels =list(p_male.value_counts().index),\n",
    "                 bbox_to_anchor = [0.85,0.3,0.4,1],\n",
    "                 facecolor = fig.get_facecolor(),\n",
    "                 title = 'BMI',\n",
    "                fontsize =  15)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "emerging-equipment",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "overhead-christianity",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "mighty-director",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "orange-effect",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bacterial-engine",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "banned-tattoo",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "specified-springer",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "hollow-decimal",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "legal-macro",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "signal-orange",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "labels = ['男1000米跑','男引体']\n",
    "_ = plt.boxplot(np.array(df_male_2iterms),\n",
    "                labels=labels,\n",
    "                notch = True,#notch参数修饰中位数的样式\n",
    "                sym = 'r+'#设置异常值形状------b对应颜色，另一个对应marker\n",
    "               )  \n",
    "plt.rcParams['font.sans-serif']=['STSong']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "completed-mason",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男引体</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>477.00</td>\n",
       "      <td>477.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.72</td>\n",
       "      <td>7.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.92</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.48</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.01</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.16</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.47</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       男1000米跑     男引体\n",
       "count   477.00  477.00\n",
       "mean      3.72    7.89\n",
       "std       0.92    4.68\n",
       "min       0.00    0.00\n",
       "25%       3.48    4.00\n",
       "50%       4.01    8.00\n",
       "75%       4.16   11.00\n",
       "max       5.47   23.00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_male_2iterms.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rough-hunger",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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